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Updated: Aug 5, 2026
10:40
A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy
Published on: April 8, 2018
Reconfigurable Ferroelectric Field-Effect Transistor Integrating Freestanding BaTiO3 and MoS2 for Neuromorphic
Ziling Chen1,2, Yiping Xiao1, Yifan Du3
1Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China.
Small (Weinheim an Der Bergstrasse, Germany)
|July 31, 2026
Summary
This study introduces a novel ferroelectric field-effect transistor (FeFET) acting as an artificial synapse for neuromorphic computing. The device offers reconfigurable non-volatile memory and low-power volatile synaptic functionalities, enabling advanced AI applications.
Area of Science:
- Materials Science
- Computer Engineering
- Artificial Intelligence
Background:
- Growing demand for computing capacity in big data and AI necessitates new computing paradigms.
- Conventional von Neumann architectures face limitations, driving research into neuromorphic computing.
- Artificial synapses are crucial components for developing efficient neuromorphic hardware.
Purpose of the Study:
- To develop a reconfigurable artificial synapse for neuromorphic computing.
- To integrate memory and computing functionalities into a single device.
- To demonstrate the device's applicability in AI tasks for autonomous systems.
Main Methods:
- Fabrication of a metal-ferroelectric-metal-insulator-semiconductor (MFMIS) FeFET device.
- Utilizing a BaTiO3 ferroelectric material and a MoS2 channel.
- Modulating input pulse width to control device behavior (non-volatile vs. volatile).
Main Results:
- The FeFET functions as a non-volatile memory with a hysteresis window > 3.8 V.
- The device operates as a volatile synaptic device with low energy consumption (1.22 fJ/event).
- Successful demonstration in CNN-based traffic sign recognition and motion direction decision-making.
Conclusions:
- The developed FeFET offers a viable strategy for creating next-generation neuromorphic devices.
- The device's reconfigurability and dual functionality address key challenges in neuromorphic hardware.
- This work paves the way for more efficient AI processing in autonomous systems.
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